U.S. patent application number 14/563917 was filed with the patent office on 2015-06-11 for methods and systems for generating virtual smart-meter data.
This patent application is currently assigned to GOOGLE INC.. The applicant listed for this patent is Google Inc.. Invention is credited to Yoky Matsuoka, Scott McGaraghan.
Application Number | 20150161020 14/563917 |
Document ID | / |
Family ID | 53271282 |
Filed Date | 2015-06-11 |
United States Patent
Application |
20150161020 |
Kind Code |
A1 |
Matsuoka; Yoky ; et
al. |
June 11, 2015 |
METHODS AND SYSTEMS FOR GENERATING VIRTUAL SMART-METER DATA
Abstract
Computational methods and systems for generating virtual
smart-meter data from operational data collected by intelligent
controllers in buildings that do not have smart meters are
disclosed. Methods and systems include collecting operational data
from a number of intelligent controllers and collecting smart-meter
data from a number of smart meters associated with the intelligent
controllers. The collected operational data and associated
smart-meter data are used to generate a mathematical model relating
operational data to smart-meter data. The mathematical model can be
used to calculate virtual smart-meter data from operational data
collected by an intelligent controller located in a building that
does not have a smart meter.
Inventors: |
Matsuoka; Yoky; (Palo Alto,
CA) ; McGaraghan; Scott; (Palo Alto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Google Inc. |
Mountain View |
CA |
US |
|
|
Assignee: |
GOOGLE INC.
Mountain View
CA
|
Family ID: |
53271282 |
Appl. No.: |
14/563917 |
Filed: |
December 8, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61913384 |
Dec 8, 2013 |
|
|
|
Current U.S.
Class: |
702/189 |
Current CPC
Class: |
G01F 11/00 20130101;
G01D 4/00 20130101; G06N 3/084 20130101; H04Q 2209/60 20130101;
G01R 21/133 20130101; G01K 17/00 20130101; G01F 15/063 20130101;
Y04S 20/30 20130101; Y02B 90/20 20130101 |
International
Class: |
G06F 11/30 20060101
G06F011/30; G01F 11/00 20060101 G01F011/00; G01K 17/00 20060101
G01K017/00; G01R 21/00 20060101 G01R021/00 |
Claims
1. A computer system comprising: one or more processors; one or
more data-storage devices; and machine-readable instructions stored
in one or more of the one or more data-storage devices that, when
executed by the one or more processors, causes the one or more
processors to perform operations comprising: receiving operational
data generated by an intelligent controller; generating virtual
smart-meter data as a function of the operational data, wherein the
function relates operational data to smart-meter data based on
operational data and smart-meter data collected from a number of
intelligent controllers and associated smart meters; and storing
the virtual smart-meter data in one or more of the data-storage
devices.
2. The computer system of claim 1, wherein the operations further
comprise: retrieves extrinsic data associated with a building in
which the intelligent controller is located; and appends the
extrinsic data to the operational data.
3. The computer system of claim 1, wherein the operations further
comprise: searching real estate databases to determine extrinsic
data associated with a building in which the intelligent controller
is located, when the extrinsic data is not available; and appending
the extrinsic data to the operational data.
4. The computer system of claim 1, wherein the function further
comprises a set of weights that relate the operational data and
smart-meter data collected from the intelligent controllers and
associated smart meters.
5. The computer system of claim 4, wherein the function is
generated from a feed-forward neural network with backward
propagation with the operational data as an input layer and the
smart-meter data as an output layer.
6. The computer system of claim 4, wherein the function is a series
of polynomials that relate each element of the smart-meter data to
the operational data.
7. The computer system of claim 4, wherein the function is a series
of rational polynomials that related each element of the
smart-meter data to the operational data.
8. The computer system of claim 1, wherein the operational data
further comprises data collected from intelligent controller
sensors, control schedules, and data generated by the intelligent
controller.
9. The computer system of claim 1, wherein the intelligent
controllers further comprises an intelligent thermostat.
10. The computer system of claim 1, wherein the smart meter further
comprises one or more of a natural gas smart meter, an electricity
smart meter, or a water smart meter.
11. A method to be carried out by a computer system that includes
one or more processors and one or more data-storage devices, the
method comprising: for a number of intelligent controllers and
associated smart meters: receiving operational data from each
intelligent controller, and receiving smart-meter data from each
smart meter associated with one of the intelligent controllers;
generating a function that generally relates operational data to
smart-meter data using the operational data and associated
smart-meter data; and using the function to generate virtual
smart-meter data from operational data received from an intelligent
controller.
12. The method of claim 11, further comprising: retrieving
extrinsic data associated with a building in which the intelligent
controller is located; and appending the extrinsic data to the
operational data.
13. The method of claim 11, further comprising: searching real
estate databases to determine extrinsic data associated with a
building in which the intelligent controller is located, when the
extrinsic data is not available; and appending the extrinsic data
to the operational data.
14. The method of claim 11, wherein generating the function further
comprises computing a set of weights that relate the operational
data and associated smart-meter data collected from the intelligent
controllers and the associated smart meters.
15. The method of claim 14, wherein computing the weights further
comprises computing the weights using a feed-forward neural network
with backward propagation with the operational data as an input
layer and the smart-meter data as an output layer.
16. The method of claim 11, wherein generating the function further
comprises determining constants of a series of polynomials that
relate each element of the smart-meter data to the operational
data.
17. The method of claim 11, wherein generating the function further
comprises determining constants of rational polynomials that relate
each element of the smart-meter data to the operational data.
18. The method of claim 11, wherein the operational data further
comprises data collected from intelligent controller sensors,
control schedules, and data generated by the intelligent
controller.
19. The method of claim 11, wherein the intelligent controllers
further comprises an intelligent thermostat.
20. The method of claim 11, wherein the smart meter further
comprises one or more of a natural gas smart meter, an electricity
smart meter, or a water smart meter.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application No. 61/913,384, filed Dec. 8, 2013, the entire contents
of which is hereby incorporated herein in its entirety for all
purposes.
TECHNICAL FIELD
[0002] The present disclosure is directed to computational systems
and methods that can be used to compute virtual meter data based on
data collected from an intelligent controller.
BACKGROUND
[0003] Control systems and control theory are well-developed fields
of research and development that have had a profound impact on the
design and development of a large number of systems and
technologies, from airplanes, spacecraft, and other vehicle and
transportation systems to computer systems, industrial
manufacturing and operations facilities, machine tools, process
machinery, and consumer devices. Control theory encompasses a large
body of practical, system-control-design principles, but is also an
important branch of theoretical and applied mathematics. Various
different types of controllers are commonly employed in many
different application domains, from simple closed-loop feedback
controllers to complex, adaptive, state-space and
differential-equations-based processor-controlled control
system.
[0004] Many controllers are designed to output control signals to
various dynamical components of a system based on a control model
and sensor feedback from the system. Many systems are designed to
exhibit a predetermined behavior or mode of operation, and the
control components of the system are therefore designed, by
traditional design and optimization techniques, to ensure that the
predetermined system behavior transpires under normal operational
conditions. Many systems are not designed to produce optimal
consumption of a utility provided by a utility company.
Theoreticians, researchers, and developers of many different types
of controllers and automated systems continue to seek approaches
that enable controllers to optimize consumption of a utility.
BRIEF SUMMARY
[0005] This disclosure is directed to computational methods and
systems for generating virtual smart-meter data from operational
data collected by intelligent controllers in buildings that do not
have smart meters. Methods and systems include collecting
operational data from a number of intelligent controllers and
collecting smart-meter data from a number of smart meters. Each
smart meter monitors and collects smart-meter data relating to a
utility provided by a utility company to a building that includes
one of the intelligent controllers. A utility can be natural gas,
electricity, or water or any other service provided by a utility
company. The collected operational data and associated smart-meter
data are used to generate a mathematical model that, in general,
relates operational data to smart-meter data. The mathematical
model can be used to calculate virtual smart-meter data from
operational data collected by an intelligent controller located in
a building that does not have a smart meter. The virtual
smart-meter data can, in turn, be used to generate
intelligent-controller commands and information that are
transmitted to the intelligent controller to adjust the intelligent
controller settings to lower the cost and improve efficient use of
the utility. The virtual smart-meter data can also be used to
assess the efficiency and performance of devices and systems
controlled by the intelligent controller.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] A further understanding of the nature and advantages of the
present invention may be realized by reference to the remaining
portions of the specification and the drawings, wherein like
reference numerals are used throughout the several drawings to
refer to similar components. In some instances, a sub-label is
associated with a reference numeral to denote one of multiple
similar components. When reference is made to a reference numeral
without specification to an existing sub-label, it is intended to
refer to all such multiple similar components.
[0007] FIG. 1 illustrates a smart-home environment, according to
some embodiments.
[0008] FIG. 2 illustrates integration of intelligent controllers
with remote devices and systems according to some embodiments.
[0009] FIG. 3 illustrates information processing within the
environment of intercommunicating entities illustrated in FIG. 2,
according to some embodiments.
[0010] FIG. 4 illustrates a general class of intelligent
controllers, according to some embodiments.
[0011] FIG. 5 illustrates additional internal features of an
intelligent controller, according to some embodiments.
[0012] FIG. 6 illustrates a generalized computer architecture that
represents an example of the type of computing machinery that may
be included in an intelligent controller, server computer, and
other processor-based intelligent controllers and cloud-based
systems, according to some embodiments.
[0013] FIG. 7 illustrates features and characteristics of an
intelligent controller of the general class of intelligent
controllers, according to some embodiments.
[0014] FIG. 8 illustrates a typical control environment within
which an intelligent controller operates, according to some
embodiments.
[0015] FIG. 9 illustrates the general characteristics of sensor
output, according to some embodiments.
[0016] FIGS. 10A-10D illustrate information processed and generated
by an intelligent controller during control operations, according
to some embodiments.
[0017] FIG. 11 illustrates examples of different communication
media smart meters can use to communicate with a utility company,
according to some embodiments.
[0018] FIG. 12 illustrates a first smart home with an intelligent
controller that controls operation of a controlled entity and
includes a smart meter, according to some embodiments.
[0019] FIG. 13 illustrates various ways in which an intelligent
controller, a smart meter, a utility company, a remote system,
according to some embodiments.
[0020] FIG. 14 illustrates a number of smart-home environments with
similar intelligent controllers connected to a remote system,
according to some embodiments.
[0021] FIG. 15A illustrates a graph of an example neural
network.
[0022] FIG. 15B presents an example of a pseudocode for multilayer
feed-forward neural networks that execute learning through back
propagation.
[0023] FIG. 16 illustrates a simple example of a feed-forward
neural network.
[0024] FIG. 17 illustrates a smart home that receives virtual
smart-meter data as a result of providing operational data from an
intelligent controller 1704.
[0025] FIG. 18A illustrates a high-level interaction between an
intelligent controller and a remote system.
[0026] FIG. 18B illustrates a flow-control diagram for the routine
"generate virtual smart-meter data" called in a step of FIG.
18A.
[0027] FIG. 18C illustrates a flow-control diagram for the routine
"update G" called in a step of FIG. 18A.
[0028] FIG. 19 illustrates a perspective view of an intelligent
thermostat, according to some embodiments.
[0029] FIGS. 20A-20B illustrate the intelligent thermostat of FIG.
19 being controlled by a user, according to some embodiments.
[0030] FIGS. 21A-21D illustrate front, bottom, side, and rear
perspective views, respectively, of an intelligent thermostat,
according to some embodiments.
[0031] FIGS. 22A-22B illustrate exploded front and rear perspective
views, respectively, of the intelligent thermostat, according to
some embodiments.
[0032] FIGS. 23A-23B illustrate exploded front and rear perspective
views, respectively, of a head unit frontal assembly of the head
unit, according to some embodiments.
[0033] FIGS. 24A-24B illustrate exploded front and rear perspective
views, respectively, of a backplate of the intelligent thermostat,
according to some embodiments.
[0034] FIGS. 25A-25B illustrate exploded front and rear perspective
views, respectively, of a backplate of the intelligent thermostat,
according to some embodiments.
[0035] FIG. 26 illustrates a front view of a head unit circuit
board of the head unit, according to some embodiments.
[0036] FIG. 27 illustrates a front view of a backplate circuit
board of the backplate, according to some embodiments.
[0037] FIGS. 28A-28B present example tables of smart-meter data
recorded by a smart meter and operational data collected by an
intelligent thermostat, according to some embodiments.
[0038] FIGS. 29A-29H illustrate examples of operational data and
virtual smart-meter data that can be generated and used to adjust
schedule settings of an intelligent thermostat, according to some
embodiments.
DETAILED DESCRIPTION
[0039] The current disclosure is directed to computational methods
and systems for identifying unidentified controlled systems using
information provided by a general class of intelligent controllers
that includes many different specific types of intelligent
controllers that can be applied to, and incorporated within, the
controlled systems. Intelligent controllers control the operation
of devices, machines, systems, and organizations. The general class
of intelligent controllers to which the current disclosure is
directed include automated learning components that allow the
intelligent controllers to learn desired operational behaviors of
the devices, machines, systems, and organizations which they
control and incorporate the learned information into control
schedules.
[0040] The current disclosure presents, in addition to methods and
implementations for identifying unidentified controlled systems, a
specific example of an intelligent thermostat controller, or
intelligent thermostat, and methods employed by the general class
of intelligent controllers to which the current disclosure is
directed. The intelligent thermostat is presented as an example of
an intelligent controller.
[0041] The detailed description includes four subsections: (1) an
overview of the smart-home environment; (2) automated
control-schedule learning; (3) methods for identifying unidentified
controlled systems; and (4) an intelligent thermostat as an example
of an intelligent controller.
Overview of the Smart-Home Environment
[0042] FIG. 1 illustrates a smart-home environment. The smart-home
environment 100 includes a number of intelligent, multi-sensing,
network-connected devices. These intelligent controllers
intercommunicate and are integrated together within the smart-home
environment. The intelligent controllers may also communicate with
cloud-based smart-home control and/or data-processing systems in
order to distribute control functionality, to access
higher-capacity and more reliable computational facilities, and to
integrate a particular smart home into a larger, multi-home or
geographical smart-home-device-based aggregation.
[0043] The intelligent controllers may include one or more
intelligent thermostats 102, one or more intelligent
hazard-detection units 104, one or more intelligent
entryway-interface devices 106, smart switches, including smart
wall-like switches 108, smart utilities interfaces and other
services interfaces, such as smart wall-plug interfaces 110, and a
wide variety of intelligent, multi-sensing, network-connected
appliances 112, including refrigerators, televisions, washers,
dryers, lights, audio systems, intercom systems, mechanical
actuators, wall air conditioners, pool-heating units, irrigation
systems, and many other types of intelligent appliances and
systems.
[0044] In general, intelligent controllers include one or more
different types of sensors, one or more controllers and/or
actuators, and one or more communications interfaces that connect
the intelligent controllers to other intelligent controllers,
routers, bridges, and hubs within a local smart-home environment,
various different types of local computer systems, and to the
Internet, through which a intelligent controller may communicate
with cloud-computing servers and other remote computing systems.
Data communications are generally carried out using any of a large
variety of different types of communications media and protocols,
including wireless protocols, such as Wi-Fi, ZigBee, 6LoWPAN,
various types of wired protocols, including CAT6 Ethernet,
HomePlug, and other such wired protocols, and various other types
of communications protocols and technologies. Intelligent
controllers may themselves operate as intermediate communications
devices, such as repeaters, for other intelligent controllers. The
smart-home environment may additionally include a variety of
different types of legacy appliances and devices 140 and 142 which
lack communications interfaces and processor-based controllers.
[0045] FIG. 2 illustrates integration of intelligent controllers
with remote devices and systems. Intelligent controllers within a
smart-home environment 200 can communicate through the Internet 202
via 3G/4G wireless communications 204, through a hubbed network
206, or by other communications interfaces and protocols. Many
different types of smart-home-related data, and data derived from
smart-home data 208, can be stored in, and retrieved from, a remote
system 210, including a cloud-based remote system. The remote
system 210 may include various types of statistics, inference, and
indexing engines 212 for data processing and derivation of
additional information and rules related to the smart-home
environment. The stored data can be exposed, via one or more
communications media and protocols, in part or in whole, to various
remote systems and organizations, including charities 214,
governments 216, academic institutions 218, businesses 220, and
utilities 222. In general, the remote data-processing system 210 is
managed or operated by an organization or vendor related to
intelligent controllers or contracted for remote data-processing
and other services by a homeowner, landlord, dweller, or other
smart-home-associated user. The data may also be further processed
by additional commercial-entity data-processing systems 213 on
behalf of the smart-homeowner or manager and/or the commercial
entity or vendor which operates the remote data-processing system
210. Thus, external entities 210 and 213 may collect, process, and
expose information collected by intelligent controllers within a
smart-home environment, may process the information to produce
various types of derived results which may be communicated to, and
shared with, other remote entities 214, 216, 218, 220, and 222, and
may participate in monitoring and control of intelligent
controllers within the smart-home environment as well as monitoring
and control of the smart-home environment. Of course, in many
cases, export of information from within the smart-home environment
to remote entities may be strictly controlled and constrained,
using encryption, access rights, authentication, and other
well-known techniques, to ensure that information deemed
confidential by the smart-home manager and/or by the remote
data-processing system is not intentionally or unintentionally made
available to additional external computing facilities, entities,
organizations, and individuals.
[0046] FIG. 3 illustrates information processing within the
environment of intercommunicating entities illustrated in FIG. 2.
The various processing engines 212 within the external
data-processing system 210 can process data with respect to a
variety of different goals, including provision of managed services
302, various types of advertizing and communications 304,
social-networking exchanges and other electronic social
communications 306, and for various types of monitoring and
rule-generation activities 308. The various processing engines 212
communicate directly or indirectly with intelligent controllers
310-313, each of which may have data-consumer ("DC"), data-source
("DS"), services-consumer ("SC"), and services-source ("SS")
characteristics. In addition, the processing engines may access
various other types of external information 316, including
information obtained through the Internet, various remote
information sources, and even remote sensor, audio, and video feeds
and sources.
Automated Schedule Learning
[0047] FIG. 4 illustrates a general class of intelligent
controllers. The intelligent controller 402 controls a controlled
entity 404, such as a device, machine, system, or organization 404,
via any of various different types of output control signals and
receives information about the controlled entity and the
environment from sensor output received by the intelligent
controller 402 from sensors embedded within the controlled entity
404, the intelligent controller 402, or in the environment of the
intelligent controller 402 and/or controlled entity 404. In FIG. 4,
the intelligent controller 402 is shown connected to the controlled
entity 404 via a wire or fiber-based communications medium 406.
However, the intelligent controller 402 may be interconnected with
the controlled entity by alternative types of communications media
and communications protocols, including wireless communications. In
many cases, the intelligent controller 402 and controlled entity
404 may be implemented and packaged together as a single system
that includes both the intelligent controller and the controlled
entity. The controlled entity 404 may include multiple devices,
machines, system, or organizations and the intelligent controller
402 may itself be distributed among multiple components and
discrete devices and systems. In addition to outputting control
signals to controlled entities and receiving sensor input, the
intelligent controller 402 also provides a user interface 410-413
through which a human user or remote entity, including a
user-operated processing device or a remote automated control
system, can input immediate-control inputs to the intelligent
controller 402 as well as create and modify various types of
control schedules. In FIG. 4, the intelligent controller 402
provides a graphical-display component 410 that displays a control
schedule 416 and includes a number of input components 411-413 that
provide a user interface for input of immediate-control directives
to the intelligent controller for controlling the controlled entity
404 or entities and input of scheduling-interface commands that
control display of one or more control schedules, creation of
control schedules, and modification of control schedules.
[0048] To summarize, the general class of intelligent controllers
receive sensor input, output control signals to one or more
controlled entities, and provide a user interface that allows users
to input immediate-control command inputs to the intelligent
controller for translation by the intelligent controller into
output control signals as well as to create and modify one or more
control schedules that specify desired controlled-entity
operational behavior over one or more time periods. These basic
functionalities and features of the general class of intelligent
controllers provide a basis upon which automated control-schedule
learning, to which the current application is directed, can be
implemented.
[0049] FIG. 5 illustrates additional internal features of an
intelligent controller. An intelligent controller is generally
implemented using one or more processors 502, electronic memory
504-507, and various types of microcontrollers 510-512, including a
microcontroller 512 and transceiver 514 that together implement a
communications port that allows the intelligent controller to
exchange data and commands with one or more entities controlled by
the intelligent controller, with other intelligent controllers, and
with various remote computing facilities, including cloud-computing
facilities through cloud-computing servers. Often, an intelligent
controller includes multiple different communications ports and
interfaces for communicating by various different protocols through
different types of communications media. It is common for
intelligent controllers, for example, to use wireless
communications to communicate with other wireless-enabled
intelligent controllers within an environment and with
mobile-communications carriers as well as any of various wired
communications protocols and media. In certain cases, an
intelligent controller may use only a single type of communications
protocol, particularly when packaged together with the controlled
entities as a single system. Electronic memories within an
intelligent controller may include both volatile and non-volatile
memories, with low-latency, high-speed volatile memories
facilitating execution of machine-readable control routines by the
one or more processors and slower, non-volatile memories storing
machine-readable control routines and data that need to survive
power-on/power-off cycles. Certain types of intelligent controllers
may additionally include mass-storage devices.
[0050] FIG. 6 illustrates a generalized computer architecture that
represents an example of the type of computing machinery that may
be included in an intelligent controller, server computer, and
other processor-based intelligent controllers and cloud-based
systems. The computing machinery includes one or multiple central
processing units ("CPUs") 602-605, one or more electronic memories
608 interconnected with the CPUs by a CPU/memory-subsystem bus 610
or multiple busses, a first bridge 612 that interconnects the
CPU/memory-subsystem bus 610 with additional busses 614 and 616
and/or other types of high-speed interconnection media, including
multiple, high-speed serial interconnects. These busses and/or
serial interconnections, in turn, connect the CPUs and memory with
specialized processors, such as a graphics processor 618, and with
one or more additional bridges 620, which are interconnected with
high-speed serial links or with multiple controllers 622-627, such
as controller 627, that provide access to various different types
of mass-storage devices 628, electronic displays, input devices,
and other such components, subcomponents, and computational
resources.
[0051] FIG. 7 illustrates features and characteristics of an
intelligent controller of the general class of intelligent
controllers. An intelligent controller includes controller logic
702 generally implemented as electronic circuitry and
processor-based computational components controlled by computer
instructions stored in physical data-storage components, including
various types of electronic memory and/or mass-storage devices. It
should be noted, at the onset, that computer instructions are
machine-readable instructions stored in physical data-storage
devices and executed within processors comprise the control
components of a wide variety of modern devices, machines, and
systems, and are as tangible, physical, and real as any other
component of a device, machine, or system. Occasionally, statements
are encountered that suggest that computer-instruction-implemented
control logic is "merely software" or something abstract and less
tangible than physical machine components. Those familiar with
modern science and technology understand that this is not the case.
Computer instructions executed by processors are physical entities
stored in physical devices. Otherwise, the processors would not be
able to access and execute the instructions. The term "software"
can be applied to a symbolic representation of a program or
routine, such as a printout or displayed list of
programming-language statements, but such symbolic representations
of computer programs are not executed by processors. Instead,
processors fetch and execute computer instructions stored in
physical states within physical data-storage devices.
[0052] The controller logic 702 accesses and uses a variety of
different types of stored information and inputs in order to
generate output control signals 704 that control the operational
behavior of one or more controlled entities. The information used
by the controller logic may include one or more stored control
schedules 706, received output from one or more sensors 708-710,
immediate control inputs received through an immediate-control
interface 712, and data, commands, and other information received
from remote data-processing systems, including cloud-based
data-processing systems 713. In addition to generating control
output 704, the controller logic 702 provides an interface 714 that
allows users to create and modify control schedules and may also
output data and information to remote entities, other intelligent
controllers, and to users through an information-output
interface.
[0053] FIG. 8 illustrates a typical control environment within
which an intelligent controller operates. As discussed above, an
intelligent controller 802 receives control inputs from users or
other entities 804 and uses the control inputs, along with stored
control schedules and other information, to generate output control
signals 805 that control operation of one or more controlled
entities 808. Operation of the controlled entities may alter an
environment within which sensors 810-812 are embedded. The sensors
return sensor output, or feedback, to the intelligent controller
802. Based on this feedback, the intelligent controller 802
modifies the output control signals 805 in order to achieve a
specified goal or goals for controlled-system operation. In
essence, an intelligent controller modifies the output control
signals according to two different feedback loops. The first, most
direct feedback loop includes output from sensors that the
controller can use to determine subsequent output control signals
or control-output modification in order to achieve the desired goal
for controlled-system operation. In many cases, a second feedback
loop involves environmental or other feedback 816 to users which,
in turn, elicits subsequent user control and scheduling inputs to
the intelligent controller 802. In other words, users can either be
viewed as another type of sensor that outputs immediate-control
directives and control-schedule changes, rather than raw sensor
output, or can be viewed as a component of a higher-level feedback
loop.
[0054] There are many different types of sensors and sensor output.
In general, sensor output is directly or indirectly related to some
type of parameter, machine state, organization state, computational
state, or physical environmental parameter. FIG. 9 illustrates the
general characteristics of sensor output. As shown in a first plot
902 in FIG. 9, a sensor may output a signal, represented by curve
904, over time t, with the signal directly or indirectly related to
a parameter P, plotted with respect to the vertical axis 906. The
sensor may output a signal continuously or at intervals, with the
time of output plotted with respect to the horizontal axis 908. In
certain cases, sensor output may be related to two or more
parameters. For example, in plot 910, a sensor outputs values
directly or indirectly related to two different parameters P.sub.1
and P.sub.2, plotted with respect to axes 912 and 914,
respectively, over time, plotted with respect to vertical axis 916.
In the following discussion, for simplicity of illustration and
discussion, it is assumed that sensors produce output directly or
indirectly related to a single parameter, as in plot 902 in FIG. 9.
In the following discussion, the sensor output is assumed to be a
set of parameter values for a parameter P. The parameter may be
related to environmental conditions, such as temperature, ambient
light level, sound level, and other such characteristics. However,
the parameter may also be the position or positions of machine
components, the data states of memory-storage address in
data-storage devices, the current drawn from a power supply, the
flow rate of a gas or fluid, the pressure of a gas or fluid, and
many other types of parameters that comprise useful information for
control purposes.
[0055] FIGS. 10A-10D illustrate information processed and generated
by an intelligent controller during control operations. All the
figures show plots, similar to plot 902 in FIG. 9, in which values
of a parameter or another set of control-related values are plotted
with respect to a vertical axis and time is plotted with respect to
a horizontal axis. FIG. 10A shows an idealized specification for
the results of controlled-entity operation. The vertical axis 1002
in FIG. 10A represents a specified parameter value, P.sub.s. For
example, in the case of an intelligent thermostat, the specified
parameter value may be temperature. For an irrigation system, by
contrast, the specified parameter value may be flow rate. FIG. 10A
is the plot of a continuous curve 1004 that represents desired
parameter values, over time, that an intelligent controller is
directed to achieve through control of one or more devices,
machines, or systems. The specification indicates that the
parameter value is desired to be initially low 1006, then rise to a
relatively high value 1008, then subside to an intermediate value
1010, and then again rise to a higher value 1012. A control
specification can be visually displayed to a user, as one example,
as a control schedule.
[0056] FIG. 10B shows an alternate view, or an encoded-data view,
of a control schedule corresponding to the control specification
illustrated in FIG. 10A. The control schedule includes indications
of a parameter-value increase 1016 corresponding to edge 1018 in
FIG. 10A, a parameter-value decrease 1020 corresponding to edge
1022 in FIG. 10A, and a parameter-value increase 1024 corresponding
to edge 1026 in FIG. 10A. The directional arrows plotted in FIG.
10B can be considered to be setpoints, or indications of desired
parameter changes at particular points in time within some period
of time.
[0057] The control schedules learned by an intelligent controller
represent a significant component of the results of automated
learning. The learned control schedules may be encoded in various
different ways and stored in electronic memories or mass-storage
devices within the intelligent controller, within the system
controlled by the intelligent controller, or within remote
data-storage facilities, including cloud-computing-based
data-storage facilities. In many cases, the learned control
schedules may be encoded and stored in multiple locations,
including control schedules distributed among internal
intelligent-controller memory and remote data-storage facilities. A
setpoint change may be stored as a record with multiple fields,
including fields that indicate whether the setpoint change is a
system-generated setpoint or a user-generated setpoint, whether the
setpoint change is an immediate-control-input setpoint change or a
scheduled setpoint change, the time and date of creation of the
setpoint change, the time and date of the last edit of the setpoint
change, and other such fields. In addition, a setpoint may be
associated with two or more parameter values. As one example, a
range setpoint may indicate a range of parameter values within
which the intelligent controller should maintain a controlled
environment. Setpoint changes are often referred to as
"setpoints."
[0058] FIG. 10C illustrates a control output by an intelligent
controller that might result from the control schedule illustrated
in FIG. 10B. In this figure, the magnitude of an output control
signal S is plotted with respect to the vertical axis 1026. For
example, the control output may be a voltage signal output by an
intelligent thermostat to a heating unit, with a high-voltage
signal indicating that the heating unit should be currently
operating and a low-voltage output indicating that the heating
system should not be operating. Edge 1028 in FIG. 10C corresponds
to setpoint 1016 in FIG. 10B. The width of the positive control
output 1030 may be related to the length, or magnitude, of the
desired parameter-value change, indicated by the length of setpoint
arrow 1016. When the desired parameter value is obtained, the
intelligent controller discontinues output of a high-voltage
signal, as represented by edge 1032. Similar positive output
control signals 1034 and 1036 are elicited by setpoints 1020 and
1024 in FIG. 10B.
[0059] Finally, FIG. 10D illustrates the observed parameter
changes, as indicated by sensor output, resulting from control, by
the intelligent controller, of one or more controlled entities. In
FIG. 10D, the sensor output, directly or indirectly related to the
parameter P.sub.0, is plotted with respect to the vertical axis
1040. The observed parameter value is represented by a smooth,
continuous curve 1042. Although this continuous curve can be seen
to be related to the initial specification curve, plotted in FIG.
10A, the observed curve does not exactly match that specification
curve. First, it may take a finite period of time 1044 for the
controlled entity to achieve the parameter-valued change
represented by setpoint 1016 in the control schedule plotted in
FIG. 10B. Also, once the parameter value is obtained, and the
controlled entity directed to discontinue operation, the parameter
value may begin to fall 1046, resulting in a feedback-initiated
control output to resume operation of the controlled entity in
order to maintain the desired parameter value. Thus, the desired
high-level constant parameter value 1008 in FIG. 10A may, in
actuality, end up as a time-varying curve 1048 that does not
exactly correspond to the control specification 1004. The first
level of feedback, discussed above with reference to FIG. 8, is
used by the intelligent controller to control one or more control
entities so that the observed parameter value, over time, as
illustrated in FIG. 10D, matches the specified time behavior of the
parameter in FIG. 10A as closely as possible. The second level
feedback control loop, discussed above with reference to FIG. 8,
may involve alteration of the control specification, illustrated in
FIG. 10A, by a user, over time, either by changes to stored control
schedules or by input of immediate-control directives, in order to
generate a modified control specification that produces a
parameter-value/time curve reflective of a user's desired
operational results.
[0060] There are many types of controlled entities and associated
intelligent controllers. In certain cases, control output may
include both an indication of whether the controlled entity should
be currently operational as well as an indication of a level,
throughput, or output of operation when the controlled entity is
operational. In other cases, the control output may be simply a
binary activation/deactivation signal. For simplicity of
illustration and discussion, the latter type of control output is
assumed in the following discussion.
Methods for Generating Virtual Smart-Meter Data Based on
Intelligent Controller Collected Data
[0061] A smart meter is a meter that records smart-meter data
corresponding to consumption of utilities or services provided by a
utility company in intervals of time and communicates smart-meter
data back to the utility company for monitoring and billing
purposes. In the following description the terms "utility" and
"service" are used inter-changeably and both terms are used to
refer to natural gas, electricity, or water or any other service
provided by a utility company. Smart meters enable two-way
communication between the smart meter and the utility company or
public utility. Examples of smart meters include smart electricity
meters that measure electricity consumption, smart gas meters that
measure natural gas consumption, and smart water meters that
measure water consumption. Unlike home energy monitors, smart
meters can gather smart-meter data for remote reporting and are
capable of two-way communications between the smart meter and the
utility company. Smart meters are also different from other types
of meters, such as interval or time-of-use meters, because smart
meters collect real-time or near real-time sensor data, provide
service outage notifications, and provide service quality
monitoring. Because smart meters provide a way of measuring utility
consumption in intervals of time, utilities are able to introduce
different prices based on the time of day and season. Smart meters
also provide a number of potential benefits to households by
enabling smart-meter users to estimate bills based on consumption
and may also help users better manage their energy use by providing
up-to-date information on the cost of gas, water, and electricity
consumption in the currency of the customer's country.
[0062] Smart meters can be equipped with any one of a number of
different media for two-way communication with a utility company.
Each smart meter is able to reliably and securely communicate
smart-meter data back to a utility company and receive operating
instructions from the utility company. FIG. 11 illustrates examples
of different communication media smart meters can use to
communicate with a utility company 1100. In FIG. 11, each of the
smart homes 1101-1105 includes one of six smart meters 1107-1111
that records smart-meter data associated with electricity
consumption, water consumption, or natural gas consumption. For
example, the smart meters 1107-1111 can be smart electricity meters
that record the amount of electricity the smart homes 1101-1105
consume per interval of time. In the example of FIG. 11, each of
the smart meters 1107-1111 uses a different communication medium to
transmit smart-meter data to the utility company 1100 and receive
instructions from the utility company 1100. Smart meter 1107 is
connected to the Internet 1114 via a hubbed network 1116, such as
Wi-Fi, Ethernet, or any other Internet related network connection.
The smart meter 1107 communicates with the utility company 1100 via
the Internet 1114 and the hub 1116 located in the smart home 1101.
Smart meter 1108 communicates with the utility company 1100 using
communication satellites 1118. Smart meter 1109 communicates with
the utility company 1100 using mobile communications technologies
1120, such as 3G/4G wireless communications. Smart meter 1110
communicates with the utility company 1100 using licensed radio or
a combination of licensed and unlicensed radio 1121. Finally, smart
meter 1111 communicates with the utility company 1100 using
power-line communication 1122, which is a technique for
transmitting data on a conductor that is also used to
simultaneously carry electricity to the smart home 1105.
[0063] The smart meters 1107-1111 can use any one of a number of
different communications protocols. For example, ANSI C12.18 is an
ANSI standard protocol used for two-way communications with a smart
meter. ANSI C12.19 specifies the data tables to be used, and ANSI
C12.21 is an extension of C12.18 written for modem communications.
Other communication protocols, such as those set by the
International Electrotechnical Commission digital formats can be
used, such as IEC 61107 and IEC 62056. The Open Smart Grid Protocol
is a family of specifications published by the European
Telecommunications Standards Institute used in conjunction with the
ISO/IEC 14908 control networking standard for smart metering and
smart grid applications. In addition, TCP/IP technology can be used
as a communication platform for smart meter applications, so that
utilities can deploy multiple communication systems, while using IP
technology as a common management platform. Other communications
protocols include a single, universal connector separating the
function of the smart grid device and its communication module.
[0064] FIG. 12 illustrates a first smart home 1202 with an
intelligent controller 1204 that controls operation of a controlled
entity 1206 and includes a smart meter 1208. The smart meter 1206
can be in communication with the utility company 1100 using any one
or more of the communication techniques described above with
reference to FIG. 11. In this example, the intelligent controller
1204 is connected to the Internet 1114 via a network hub 1210 and
is in direct communication with the smart meter 1208 so that when
the smart meter 1208 transmits recorded smart-meter data to the
utility company 1100, the smart meter 1208 also transmits the same
recorded smart-meter data to the intelligent controller 1204. The
intelligent controller 1204 controls operation of the controlled
entity 1206 and generates operational data that is sent along with
the smart-meter data to a remote system 1210 via the Internet 1114.
Operational data can be raw data collected from the one or more
intelligent-controller sensors, control specifications and control
schedules input to the intelligent controller 1204 by the
intelligent-controller user, and any other data generated by the
intelligent controller 1204 in response to the raw data collected
by the one or more sensors, control specification and control
schedules. The remote system 1210 can represent one or both of the
entities 210 and 213 described above with reference to FIG. 2, or
represent another cloud-based computer system that processes data
provided by the intelligent controller 1204. Because the smart home
1202 includes a smart meter 1206, the smart home 1202 occupant may
be able to access the smart-meter data from a user account provided
by the utility company, or because the smart meter 1206 sends
smart-meter data to the intelligent controller 1204, the
intelligent controller 1204 can display the smart-meter data on the
intelligent controller 1204 user interface.
[0065] The remote system 1210 is not limited to receiving
smart-meter data from intelligent controllers, as described above
with reference to FIG. 11. FIG. 13 illustrates various ways in
which an intelligent controller 1301, a smart meter 1302, a utility
company 1303, a remote system 1304, and a user 1305 can exchange
data. The user 1305 represents an occupant or owner of smart home
equipped with the intelligent controller 1301 and the smart meter
1302. Directional arrows 1307-1315 represent communications between
the entities. In particular, directional arrows 1307 and 1308
represent communications between the smart meter 1302 and the
utility company 1303. The communications can be carried out using
any one or the media and protocols described above with reference
to FIG. 11. Directional arrow 1309 represents the user's 1305
access to an on-line user interface that enables the user 1305 to
access the user's utility company account information and may
provide the user access to smart-meter data and may present the
user 1305 with time-interval costs based on different time-interval
billing rates. Directional arrows 1310 and 1311 represent the
intelligent controller 1301 displaying notices from the intelligent
controller 1301 to the user 1305 and the intelligent controller
1301 receiving operational settings, such as adjusting a control
schedule, from the user 1305. Directional arrows 1312 and 1313
represent the remote system 1304 receiving operational data from
the intelligent controller 1301 and the intelligent controller 1301
receiving commands and information from the remote system 1304.
Directional arrows 1314 and 1315 represent the remote system 1304
providing the user 1305 access to operational data collected by the
intelligent controller 1301 and the user 1305 entering information
to the remote system 1304. For example, when the user 1305
purchases and installs the intelligent controller 1301 the user
1305 can register the intelligent controller 1301 with the
manufacturer or vendor by providing address information and provide
information on the layout of the smart home. Dashed-directional
arrows 1316 and 1317 represent ways in which smart-meter data
generated by the smart meter 1302 can be retrieved by the remote
system 1304. As described above in the example of FIG. 12, the
smart meter 1302 can directly transmit smart-meter data to the
intelligent controller 1301 when the smart meter 1302 transmits
smart-meter data to the utility company 1303, and the remote system
1304 can retrieve the smart-meter data from the intelligent
controller 1301. Alternatively, the user 1305 can grant the remote
system 1304 access to the user's account with the utility company
1303, enabling the remote system 1304 to retrieve the smart-meter
data from the utility company 1303.
[0066] Returning to FIG. 12, a second smart home 1212 has an
intelligent controller 1214 that controls operation of a controlled
entity 1216, but, unlike the first smart home 1202, the second
smart home 1212 does not have a smart meter. The intelligent
controller 1214 is connected to the Internet 1114 via a network hub
1210 and sends operational data to the remote system 1210. Methods
and systems described below are directed to using operational data
and smart-meter data collected from a number of smart homes that
have smart meters and similar intelligent controllers to formulate
a mathematical-based model that can be used to generate virtual
smart-meter data from operational data provided by intelligent
controllers in smart homes that do not have smart meters, such as
the smart home 1212. In particular, FIG. 14 illustrates a number of
smart-home environments with similar intelligent controllers
connected to a remote system 1402 via the Internet 1404, as
described above with reference to FIG. 2. The phrase "similar
intelligent controllers" refers to intelligent controllers that
have common features, operation, and produce the same kind of
operational data. For example, intelligent thermostats are an
example of similar intelligent controllers, intelligent
hazard-detection units are also an example of similar intelligent
controllers, and smart switches are another example of similar
intelligent controllers. Shading represents smart homes that have
an intelligent controller and a smart meter, and unshaded smart
homes have an intelligent controller but do not have a smart meter.
Operational data and smart-meter data associated with the shaded
smart homes equipped with intelligent controllers and smart meters
is collected by the remote system 1402. The operational data and
smart-meter data collected from the smart homes with intelligent
controllers and smart meters is used to formulate a mathematical
model that can, in turn, be used to generate virtual smart-meter
data from the operational data collected from the unshaded smart
homes that have intelligent controllers but do not have smart
meters. The virtual smart-meter data approximates the smart-meter
data that would be collected by a smart meter.
[0067] Mathematical models that relate operational data to
smart-meter data are now described. Smart-meter data associated
with a smart-home environment can be represented mathematically
using column vector notation as follows:
S = [ s 1 s 2 s 3 M s N ] ( 1 ) ##EQU00001##
where s.sub.i represents the ith smart-meter data element, and N
represents the number of smart-meter data elements.
[0068] A smart-meter data element s.sub.i is a numerical value that
represents the amount of a utility provided by a utility company to
a user. For example, the element s.sub.1 can be a numerical value
that represents the total amount of electricity, water or natural
gas consumed in a 24 hour period. The element s.sub.2 can be a
numerical value that represents the amount of electricity, water,
or natural gas consumed in a time interval, such as a one hour time
interval.
[0069] It should be noted that the operational data may also
include extrinsic data regarding the layout, year of the home,
controlled entity model, and any other data that relates the
building in which the intelligent controller is installed and the
controlled entity. For example, if the intelligent controller is an
intelligent thermostat and the controlled entity is a natural gas
heating unit, relevant extrinsic data that can be included in the
operational data can be the layout of the home, such as square
footage and number of rooms, year the home was built, and the
heating unit model.
[0070] On the other hand, the operational data collected and/or
generated by an intelligent controller can also be represented
mathematically using column vector notation as follows:
Q = [ q 1 q 2 q 3 M q M ] ( 2 ) ##EQU00002##
where q.sub.j represents the jth operational data element, and M
represents the number of operational data elements.
[0071] An operational data element q.sub.j is a numerical value
that represents a particular type of raw data collected by sensors
and/or data generated by the intelligent controller in response to
the raw data collected by the one or more sensors, control
specification and control schedules. For example, the element
q.sub.1 can be a numerical value that represents a setpoint when a
controlled entity is to be turned on or off. The element q.sub.2
can represent a parameter, such as temperature, measured by a
sensor at a particular time of day.
[0072] Smart-meter data can be mathematically related to
operational data by a function that maps the operational data to
the smart-meter data as follows:
=G[] (3)
where G represents a function that relates the operational data to
each element of the smart-meter data.
[0073] The function G may also have an inverse G.sup.-1 that maps
the smart-meter data to the operational data as follows:
=G.sup.-1[] (4)
[0074] In certain embodiment, the function G can be computed using
neural network-based techniques to produce a mathematical model of
the relationship between known operational data and smart-meter
data. Neural networks are a computational modeling technique that
changes structure during a learning phase and can be used to model
the complex relationship between the smart-meter data and the
operational data . Learning is achieved by iteratively adjusting
the numerical values of weights in a network until the
network-action computing performance reaches an acceptable
threshold or after a set number of iterations have been executed.
FIG. 15A illustrates a graph of an example neural network 1500 that
represents a network of weighted relationships between the
smart-meter data and the operational data . In this example, the
neural network 1500 includes an input layer 1502, two hidden layers
1504 and 1506, and an output layer 1508. The input layer 1502 is
composed of nodes that correspond to the operational data elements
of , and the output layer 1508 is composed of nodes that correspond
to the smart-meter data elements of . Hidden layers 1504 and 1506
are composed of nodes that represent hidden units denoted by
a.sub.i. Hidden layer 1504 is composed of F nodes that correspond
to F hidden units, and hidden layer 1506 is composed of H nodes
that correspond to H hidden units, where F and H are positive
integers. Certain pairs of nodes are connected by links or edges,
such as link 1510, that represent weigths denoted by W'.sub.ji.
Each weight determines the strength and sign of a connection
between two nodes. It should be noted that neural networks are not
limited to two hidden layers and a fixed number of nodes in each
layer. The number of hidden layers and number of nodes in each
hidden layer can be selected based on computation efficiency. In
other words, the number of hidden layers can range from a few as
one to some number greater than two, and the number of nodes in
each hidden layer is not limited.
[0075] FIG. 15B presents an example of a pseudocode for multilayer
feed-forward neural networks that execute learning through back
propagation. The number of layers in the neural network is denoted
by a positive integer L. It should be noted that this pseudocode is
not intended to limit the number of steps or to be exhaustive of
the numerous ways in which a multilayer feed-forward neural network
can be implemented to compute a relationship between operational
data and smart-meter data, but is instead provided as one example
of a computation approach to computing the relationship between
operational data and smart-meter data.
[0076] In step 1, the weights W'.sub.ji are initialized to values
between 0 and 1. The weights can be initialized using a random
number generator that assigns a randomly computed value between 0
and 1 to each of the weights. The initialization is performed one
time and the weights are computed for a set of corresponding
operational and smart-meter data associated with a type of
intelligent controller. In step 2, steps 3 through 10 are repeated
to calculate, or train, a set of weights that defines a
relationship between the set of operational and smart-meter data
associated with smart homes having smart meters and similar
intelligent controllers.
[0077] In step 3, the smart-meter data and operational data are
retrieved from the database. Each operational data element is a
node in the input layer, and each smart-meter data element is a
node in the output layer, as illustrated in FIG. 15A. In the
for-loop of step 4, each node or operational data element q.sub.j
in the input layer is assigned to a hidden unit a.sub.j. In the
for-loop of step 5, for each layer l between 2 and L, h(sum.sub.i)
is calculated and assigned to a hidden unit a.sub.i for each node,
where h is an activation function. The activation function, h, can
be a threshold activation function that outputs 1 when the input is
positive and 0 otherwise. Alternatively, the activation function
can be a sigmoid function. Examples of sigmoid activation functions
include h(sum.sub.i)=tan h(sum.sub.i) and
h(sum.sub.i)=1/(1+e.sup.-sum.sup.i). In the for-loop of step 6, for
each node in the output layer an error Error.sub.i and a modified
error .DELTA..sub.i are calculated, where h' represents the first
derivative of the activation function h. The modified error
corresponds to a fraction of the error in the nodes of the output
layer. Step 7 is a for-loop that executes back propagation and
weight updates in steps 8 and 9 beginning with L-1 and ending with
the input layer (i.e., l=1). In the for-loop of step 8, the
modified error is calculated for each hidden layer l, and in the
for-loop of step 9, the weights are updated for each node in the
hidden layer l+1. In step 10, when the Error.sub.i for each node in
the output layer is less than a defined error threshold, steps 3
through 10 are repeated for another set of smart-meter data and
operational data. Otherwise, if one of the errors Error.sub.i
exceeds or equals the predefined threshold, steps 5 through 10 are
repeated. Note that in other embodiments rather than using a
threshold, steps 5 through 9 can be repeated for a preset number of
iterations.
[0078] Steps 3 through 10 can be repeated for a large number of
smart homes with intelligent controllers and smart meters in order
to computationally generate a set of weights that define a
relationship between operational and smart-meter data associated
with a particular type of intelligent controller. The resulting set
of weights can then be used to calculate virtual smart-meter data
for intelligent-controller users who do not have smart meters. FIG.
16 shows a simple example of a feed-forward neural network 1600
with an operational data set composed of two operational data
elements q.sub.1 and q.sub.2 and a smart-meter data set also
composed of two smart-meter data elements s.sub.1 and s.sub.2. In
this example only one hidden layer composed of three hidden units
a.sub.1, a.sub.2, and a.sub.3 is selected to determine a set of
weights. In FIG. 16, a first set of weights that link the
operational data elements q.sub.1 and q.sub.2 to the hidden units
a.sub.1, a.sub.2, and a.sub.3 are denoted by W'.sub.11.sup.(1),
W'.sub.21.sup.(1), W'.sub.12.sup.(1), W'.sub.13.sup.(1),
W'.sub.22.sup.(1), and, W'.sub.23.sup.(1), and a second set of
weights that link the hidden units a.sub.1, a.sub.2, and a.sub.3 to
the smart-meter data elements s.sub.1 and s.sub.2 are denoted by
W'.sub.11.sup.(2), W'.sub.21.sup.(2), W'.sub.31.sup.(2),
W'.sub.12.sup.(2), W'.sub.22.sup.(2), and, W'.sub.32.sup.(2). The
weights can be calculated using a feed-forward neural network with
back propagation, such as the pseudocode described above with
reference to FIG. 15B, for a large number of smart homes with
similar or the same intelligent controllers and smart meters. Once
weights have been determined, virtual smart-meter data can be
calculated for a smart home that has an intelligent controller but
is not equipped with a smart meter. Virtual smart-meter data
s.sub.1 and s.sub.2 can be calculated by first calculating the
hidden units a.sub.1, a.sub.2, and a.sub.3 as a function of the
operational data and the first set of weights, as represented by a
first set of equations 1602. Next, virtual smart-meter data s.sub.1
and s.sub.2, represented by equations 1604, is calculated as a
function of the hidden units a.sub.1, a.sub.2, and a.sub.3
calculated in equations 1602 and the second set of weights. FIG. 16
includes equations 1606 that combines equations 1602 and 1604 to
calculate virtual smart-meter data s.sub.1 and s.sub.2 as a
function of the operational data and the two sets of weights.
[0079] The simple example in FIG. 16 demonstrates how an unknown
smart-meter data element can be calculated from operational data
and a trained set of weights. In practice, a feed-forward neural
network with backward propagation can be used to calculate
operational data from smart-meter data by identifying the
smart-meter data as the input layer and the operational data as the
output layer and following the same computational method described
above with reference to FIG. 15B. In other words, a feed-forward
neural network with backward propagation can be used to generate a
mathematical model that relates operational data as the output
layer and smart-meter data as the input layer.
[0080] The function G defined in Equation (3) can also represent N
functions with each function mapping the operational data to one
smart-meter data element of :
S = G [ Q ] = [ s 1 ( Q ) s 2 ( Q ) s 3 ( Q ) M s N ( Q ) ] ( 5 )
##EQU00003##
[0081] In Equation (5), each smart-meter data element is calculated
as a function of the operational data. The inverse function
G.sup.-1 can also be a series of M functions with each function
mapping the smart-meter data to one smart-meter data element of
:
Q = G - 1 [ S ] = [ q 1 ( S ) q ( S ) q 3 ( S ) M q M ( S ) ] ( 6 )
##EQU00004##
In Equation (6), each operational data element is calculated as a
function of the smart-meter data.
[0082] In certain embodiments, the function G can represent a
matrix given by:
S = G [ Q ] = W Q where ( 7 ) W = [ W 11 W 12 W 13 L W 1 M W 21 W
22 W 23 W 31 W 32 W 33 M M O W N 1 L W NM ] ( 8 ) ##EQU00005##
and with the W.sub.ij's are real-valued weights.
[0083] In other words, each smart-meter data element given in
Equation (5) can be a linear function of the operational data as
follows:
s i ( Q ) = j = 1 M W ij q j ( 9 ) ##EQU00006##
[0084] Likewise, the inverse function S.sup.-1 can represent a
matrix given by:
S = G - 1 [ Q ] w Q where ( 10 ) w = [ w 11 w 12 w 13 L w 1 N w 21
w 22 w 23 w 31 w 32 w 33 M M O w M 1 L w MN ] ( 11 )
##EQU00007##
[0085] and the w.sub.ji's are real-valued weights.
[0086] In other words, each operational data element given in
Equation (6) can be a linear function of the smart-meter data as
follows:
q j ( S ) = i = 1 N w ji s i ( 12 ) ##EQU00008##
[0087] The weights W.sub.ij's and w.sub.ji's in Equations (9) and
(12), respectively, can be determined using the numerical
optimization method, such as the conjugate gradient method, or by
empirically adjusting the weights using smart-meter data and
operational data obtained from a number of smart homes equipped
with smart meters and an intelligent controller. In other
embodiments, the weights W.sub.ij's in Equation (9) can be
determined using numerical optimization method or empirically and
the weights w.sub.ji's in matrix Equation (11) can be calculated by
computing a pseudoinverse of the matrix Equation (8). In other
embodiments, the weights w.sub.ij's in Equation (11) can be
determined using numerical optimization method or empirically and
the weights W.sub.ji's in matrix Equation (9) can be calculated by
computing a pseudoinverse of the matrix Equation (11).
[0088] In other embodiments, more complex polynomial functions can
be formulated to characterize the relationships between the
smart-meter data and the operational data. In other words, each
smart-meter data element given in Equation (5) can be a polynomial
function of the operational data:
s i ( Q ) = j = 1 M B ij q j A ij C i ( 13 ) ##EQU00009##
where A.sub.ij, B.sub.ij, and C.sub.i are real-valued
constants.
[0089] In addition, each operational data element given in Equation
(6) can be a polynomial function of the smart-meter data:
q j ( S ) = i = 1 N b ji s i a ji c j ( 14 ) ##EQU00010##
where a.sub.ji, b.sub.ji, and c.sub.j are real-valued
constants.
[0090] The constants A.sub.ij, R.sub.ij, and C.sub.i in Equation
(13) and the constants a.sub.ji b.sub.ji, and c.sub.j in Equation
(14) can be determined empirically by adjusting the constants so
that smart-meter data and operational data obtained from a large
number of test participants satisfy Equations (5) and (6).
[0091] In other embodiments, rational functions can be formulated
to characterize the relationships between the smart-meter data and
the operational data. For example, each smart-meter data element
given in Equation (5) can be calculated using a rational polynomial
function of the operational data:
s i ( Q ) = j = 1 M B ij q j A ij + C i j = 1 M D ij q j E ij + J i
( 15 ) ##EQU00011##
where A.sub.ij, B.sub.ij, C.sub.i, D.sub.ij, E.sub.ij, and J.sub.i
are real-valued constants.
[0092] In addition, each operational data element given in Equation
(6) can be calculated using a rational polynomial function of the
smart-meter data:
q j ( S ) = i = 1 N b ji s i a ji + c j i = 1 N e ji s i f ji + g j
( 16 ) ##EQU00012##
where a.sub.ji, b.sub.ji, c.sub.j, e.sub.ji, f.sub.ji, and g.sub.j
are real-valued constants.
[0093] The constants in Equations (15) and (16) can be determined
empirically by adjusting the constants so that smart-meter data and
operational data obtained from a large number of test participants
satisfy Equations (5) and (6).
[0094] FIG. 17 illustrates a smart home 1702 that receives virtual
smart-meter data ' as a result of providing operational data from
an intelligent controller 1704. In this example, the intelligent
controller 1704 collects and transmits operational data over the
Internet 1706 to a remote system 1708. Based on the operational and
smart-meter data collect from a number of smart homes with smart
meters and similar intelligent controllers, a function G is
calculated as described above and is used to calculate virtual
smart-meter data '. The virtual smart-meter data ' is transmitted
back to the user 1710. The user 1710 can view the virtual
smart-meter data ' using the intelligent controller 1704 user
interface or using a user interface provided by the intelligent
controller manufacture or vendor. The virtual smart-meter data '
can also be used to adjust the operational settings of the
intelligent controller 1704 to lower the cost of operating a
controlled entity 1712.
[0095] Note that extrinsic data may also be appended to the
operational data. Extrinsic data can be data that mathematically
represents the layout or the home 1702, year the home was built,
controlled entity 1712 model, and any other data that relates to
the controlled entity 1712 and the home 1702. For example, if the
intelligent controller is an intelligent thermostat and the
controlled entity is a natural gas, forced air furnace, relevant
extrinsic data can be appended to the operational data and used by
the remote system to calculate G. The extrinsic data can be the
layout of the home, such as square footage and number of rooms,
year the home was built, and the furnace model.
[0096] Next, FIGS. 18A-18C illustrate an example implementation of
a method that incorporates the above described mathematical models
for generating virtual smart-meter data. At the onset, it should be
noted that the following implementation is but one of many
different possible implementations that can be obtained by varying
many different design and implementation parameters, including
modular organization, control structures, data structures,
programming language, hardware components, firmware, and other such
design and implementation parameters.
[0097] FIG. 18A illustrates a high-level interaction between an
intelligent controller and a remote system. Left-hand column 1801
represents steps executed by an intelligent controller, and
right-hand column 1802 represents steps executed by a remote system
interacting with the intelligent controller. In step 1803, when the
intelligent controller is in communication with a smart meter, as
described above with reference to FIG. 12, and the intelligent
controller receives smart-meter data generated by the smart meter,
control flows to step 1804. Otherwise, when the intelligent
controller is not in communication with a smart meter, or the
building does not have a smart meter, control flows to step 1806.
In step 1804, the intelligent controller collects stored
smart-meter data. In step 1805, the intelligent controller
transmits the collected smart-meter data to the remote system. Note
also that the intelligent controller can be used to transmit the
smart-meter data to a utility company, as described above with
reference to FIG. 12. In step 1806, the intelligent controller
collects stored operational data. In step 1807, the intelligent
controller transmits the operational data to the remote system, as
described above with reference to FIG. 2.
[0098] In step 1808 of column 1802, the remote system receives and
stores the operational data associated with the intelligent
controller in a database. In step 1809, when the remote system
receives smart-meter data from the intelligent controller, the
remote system stores the smart-meter data and control flows to step
1810. On the other hand, when the remote system does not receive
smart-meter data control flows to step 1811. In step 1811, when the
remote system is able to retrieve smart-meter data associated with
the building from the utility company as described above with
reference to FIG. 13, the remote system stores the smart-meter data
and control flows to step 1810. Otherwise, control flows to step
1812. In step 1810, a routine called "update G" is called to update
the weights or constants associated with a function G based on the
operational data and smart-meter data. The function G relates
operational data to smart-meter data as described above with
reference to FIG. 15 and Equations (1) through (16). In step 1812,
a routine called "generate virtual smart-meter data" is called to
calculate virtual smart-meter data associated with a home that has
an intelligent controller but does not have a smart meter. In step
1813, the virtual smart-meter data generated in step 1812 is
transmitted to the intelligent controller. In step 1813, the
virtual smart-meter data generated in step 1812 can alternatively
be sent to the intelligent controller manufacturer, vendor or
organization that provides the intelligent-controller user the
virtual smart-meter data, as described above with reference to FIG.
13. In step 1814, the remote system generates
intelligent-controller commands and information based on the
virtual smart-meter data and the operational data. The intelligent
controller commands and information include instructions that can
be used by the intelligent controller to change the control
schedule and settings to better match the habits of people living
the home, improve operation of a controlled entity, and decrease
demand on the services provided by utility companies.
Alternatively, when control flows through step 1810, the remote
system generates intelligent-controller commands and information
based on the smart-meter data and the operational data in step
1814. In step 1815, the intelligent controller commands and
information are transmitted to the intelligent controller.
[0099] Returning to column 1801, in step 1816, virtual smart-meter
data transmitted to the intelligent controller from the remote
system is received and stored. In step 1817, the virtual
smart-meter data can presented on the intelligent-controller user
interface. Alternatively, in step 1817, the intelligent controller
can send the virtual smart-meter data over the home network hub to
a smart phone, tablet, or personal computer in order to enable the
intelligent-controller user to view the virtual smart-meter data.
In step 1818, the intelligent controller receives intelligent
controller commands and information transmitted from the remote
system. In step 1819, the intelligent controller adjust control
schedules and control settings based on the intelligent controller
commands and information generated by the remote system.
[0100] FIG. 18B illustrates a flow-control diagram for the routine
"generate virtual smart-meter data" called in step 1812 of FIG.
18A. In step 1820, when extrinsic data regarding the home in which
the intelligent controller is known, control flows to step 1825.
Extrinsic data can be home layout, such as the number of floors and
rooms, the year the home was built, square footage, and the address
or zip code the home is located in. If extrinsic data is not
available, control flows to step 1821. In step 1821, when address
information has been provide by the intelligent-controller user,
control flows to step 1822, otherwise, control flows to step 1825.
In step 1822, the remote system uses address information provided
by the intelligent-controller user to search real estate databases
to determine the home layout, year the home was built, and square
footage. In step 1823, the remote system database can be updated to
include the extrinsic data. In step 1824, the extrinsic data
obtained in step 1822 can be appended to the operational data. In
step 1825, virtual smart-meter data ' is computed. The virtual
smart-meter data can be computed using weights generated using
weights obtained from a feed-forward neural network as described
above in the example of FIG. 16. Alternatively, the virtual
smart-meter data can be computed from Equations (6), (13), or (15).
It should be noted that when no extrinsic data is available or can
be determined, only the operational data provided by the
intelligent controller is used to calculate the virtual smart-meter
data.
[0101] FIG. 18C illustrates a flow-control diagram for the routine
"update G" called in step 1810 of FIG. 18A. Steps 1830-1834 are
essentially the same as the steps 1820-1824 described above with
reference to the routine illustrated in FIG. 18B. In step 1835,
smart-meter data and operational data are used to update the
function G. For example, the smart-meter data and the operational
data can be used to recomputed the weights obtained using a
feed-forward neural network as described above in the example of
FIG. 15. Alternatively, the smart-meter data and the operational
data can be used to recomputed or adjust the constants associated
with the Equations (6), (13), or (15). It should be noted that when
no extrinsic data is available or can be determined, only the
operational data provided by the intelligent controller is used to
update the function G.
An Intelligent Thermostat as an Example of an Intelligent
Controller
[0102] In the following description, an intelligent thermostat is
described as just one example of an intelligent controller that can
be used with the above describes methods. The intelligent
thermostat described below is equipped with a selectively layered
functionality that exposes unsophisticated users to a simple user
interface, but provides advanced users with an ability to access
and manipulate many different energy-saving and energy tracking
capabilities. Even for the case of unsophisticated users who are
only exposed to the simple user interface, the intelligent
thermostat provides advanced energy-saving functionality that runs
in the background. The intelligent thermostat uses multi-sensor
technology to learn the heating and cooling environment in which
the intelligent thermostat is located and to optimize energy-saving
settings.
[0103] The intelligent thermostat also learns about the users,
beginning with a setup dialog in which the user answers a few
simple questions, and then continuing, over time, using
multi-sensor technology to detect user occupancy patterns and to
track the way the user controls the temperature using schedule
changes and immediate-control inputs. On an ongoing basis, the
intelligent thermostat processes the learned and sensed
information, automatically adjusting environmental control settings
to optimize energy usage while, at the same time, maintaining the
temperature within the environment at desirable levels, according
to the learned occupancy patterns and comfort preferences of one or
more users. Advantageously, the selectively layered functionality
of the intelligent thermostat allows for effective operation in a
variety of different technological circumstances within home and
business environments. For simple environments having no wireless
home network or Internet connectivity, the intelligent thermostat
operates effectively in a standalone mode, learning and adapting to
an environment based on multi-sensor technology and user input.
However, for environments that have home network or Internet
connectivity, the intelligent thermostat operates effectively in a
network-connected mode to offer additional capabilities.
[0104] When the intelligent thermostat is connected to the Internet
via a home network, such as through IEEE 802.11 (Wi-Fi)
connectivity, the intelligent thermostat may: (1) provide real-time
or aggregated home energy performance data to a utility company,
intelligent thermostat data service provider, intelligent
thermostats in other homes, or other data destinations; (2) receive
real-time or aggregated home energy performance data from a utility
company, intelligent thermostat data service provider, intelligent
thermostats in other homes, or other data sources; (3) receive new
energy control instructions and/or other upgrades from one or more
intelligent thermostat data service providers or other sources; (4)
receive current and forecasted weather information for inclusion in
energy-saving control algorithm processing; (5) receive user
control commands from the user's computer, network-connected
television, smart phone, and/or other stationary or portable data
communication appliance; (6) provide an interactive user interface
to a user through a digital appliance; (7) receive control commands
and information from an external energy management advisor, such as
a subscription-based service aimed at leveraging collected
information from multiple sources to generate energy-saving control
commands and/or profiles for their subscribers; (8) receive control
commands and information from an external energy management
authority, such as a utility company to which limited authority has
been voluntarily given to control the intelligent thermostat in
exchange for rebates or other cost incentives; (9) provide alarms,
alerts, or other information to a user on a digital appliance based
on intelligent thermostat-sensed HVAC-related events; (10) provide
alarms, alerts, or other information to the user on a digital
appliance based on intelligent thermostat-sensed non-HVAC related
events; and (11) provide a variety of other useful functions
enabled by network connectivity.
[0105] The subject matter of the instant disclosure is related to
the subject matter of the following commonly assigned applications,
each of which is incorporated by reference herein: U.S. Ser. No.
13/034,674 filed Feb. 24, 2011 (Ref. No. NES0006-US); U.S. Ser. No.
13/034,678 filed Feb. 24, 2011 (Ref. No. NES0007-US); U.S. Ser. No.
13/269,501 filed Oct. 7, 2011 (Ref. No. NES0120-US); U.S. Ser. No.
13/632,093 filed Sep. 30, 2012 (Ref. No. NES0122-US); U.S. Ser. No.
13/632,028 filed Sep. 30, 2012 (Ref. No. NES0124-US); U.S. Ser. No.
13/275,307 filed Oct. 17, 2011 (Ref. No. NES0160-US); U.S. Ser. No.
13/632,041 filed Sep. 30, 2012 (Ref. No. NES0162-US); U.S. Ser. No.
13/467,025 filed May 8, 2012 (Ref. No. NES0177-US); U.S. Ser. No.
13/988,435 filed Jan. 3, 2012 (Ref. No. NES0190-US); PCT
Application Ser. No. PCT/US12/00007 filed Jan. 3, 2012 (Ref. No.
NES0190-PCT); U.S. Ser. No. 13/866,578 filed Apr. 19, 2013 (Ref.
No. NES0211-US); U.S. Ser. No. 13/632,070 filed Sep. 30, 2012 (Ref.
No. NES0234-US); U.S. Ser. No. 13/624,878 filed Sep. 21, 2012 (Ref.
No. NES0236-US); U.S. Ser. No. 13/842,213 filed Mar. 15, 2013 (Ref.
No. NES0253-US); and U.S. Ser. No. 13/866,199 filed Apr. 19, 2013
(Ref. No. NES0335-US).
[0106] FIG. 19 illustrates a perspective view of an intelligent
thermostat found to be particularly advantageous for use in
conjunction with one or more of the described embodiments. The
intelligent thermostat 1900 has a sleek, elegant appearance. The
intelligent thermostat 1900 comprises a circular main body 1908
with a diameter of about 8 cm and that has a visually pleasing
outer finish, such as a satin nickel or chrome finish. A cap-like
structure comprising a rotatable outer ring 1906, a sensor ring
1904, and a circular display monitor 1902 is separated from the
main body 1908 by a small peripheral gap 1910. The outer ring 1906
may have an outer finish identical to that of the main body 1908,
while the sensor ring 1904 and circular display monitor 1902 may
have a common circular glass (or plastic) outer covering that is
gently arced in an outward direction and that provides a sleek yet
solid and durable-looking overall appearance. The sensor ring 1904
contains any of a wide variety of sensors, including infrared
sensors, visible-light sensors, and acoustic sensors. The glass or
plastic that covers the sensor ring 1904 may be smoked or mirrored
such that the sensors themselves are not visible to the user. An
air venting functionality may be provided, via the peripheral gap
1910, which allows the ambient air to be sensed by the internal
sensors without the need for gills or grill-like vents.
[0107] FIGS. 20A-20B illustrate the intelligent thermostat 1900 of
FIG. 19 as it is being controlled by a user. The intelligent
thermostat 1900 is controlled by two types of user input: (1) a
rotation of the outer ring 1906 (FIG. 20A); and (2) an inward push
on the outer ring 1906 (FIG. 20B) until an audible and/or tactile
"click" occurs. The inward push may cause the outer ring 1906 to
move forward, while in another implementation, the entire cap-like
structure, including both the outer ring 1906 and the glass
covering of the sensor ring 1904 and circular display monitor 1902,
move inwardly together when pushed. The sensor ring 1904, the
circular display monitor 1902, and the common glass covering do not
rotate with outer ring 1906 in one implementation. In the drawings
shown herein, the "z" direction is outward from the wall, the "y"
direction is the toe-to-head direction relative to a walk-up user,
and the "x" direction is the user's left-to-right direction.
[0108] By rotation of the outer ring 1906, or ring rotation, and
inward pushing of the outer ring 1906, or inward click, the
intelligent thermostat 1900 can receive all necessary information
from the user for basic setup and operation. The outer ring 1906 is
mechanically mounted in a manner that provides a smooth yet viscous
feel to the user, for further promoting an overall feeling of
elegance while also reducing spurious or unwanted rotational
inputs. The intelligent thermostat 1900 recognizes three
fundamental user inputs: (1) ring rotate left, (2) ring rotate
right, and (3) inward click. In other implementations, more complex
fundamental user inputs can be recognized, such as double-click or
triple-click inward presses and speed-sensitive or
acceleration-sensitive rotational inputs.
[0109] FIGS. 21A-21D illustrate an intelligent thermostat 2100 that
is similar in certain respects to the intelligent thermostat 1900
of FIGS. 19-20B, supra, the intelligent thermostat 2100 having a
visually pleasing, smooth, sleek and rounded exterior appearance
while at the same time including multiple environmental sensors,
multiple occupancy sensors, intelligent processing and logging
capability, and network connectivity capability. FIG. 21A is front
view, FIG. 21B is a bottom elevation view, FIG. 21C is a right side
elevation view, and FIG. 21D is perspective view of intelligent
thermostat 2100. Intelligent thermostat 2100 is wall mountable, is
circular in shape, and has an outer rotatable ring 2112 for
receiving user input. The outer rotatable ring 2112 allows the user
to make adjustments, such as selecting a new setpoint temperature.
Rotation of the outer ring 2112 can be sensed by an onboard optical
finger navigation (OFN) module 2161 that is configured and
positioned to sense movement of a textured surface on an inner
surface of the outer ring 2112. The front face of the intelligent
thermostat 2100 comprises a clear cover 2114 that according to some
embodiments is polycarbonate, and a Fresnel lens 2110 having an
outer shape that matches the contours of the curved outer front
face of the intelligent thermostat 2100. For some embodiments, an
inward pressing of the outer ring 2112 and/or the clear cover 2114
of the intelligent thermostat 2100 causes inward motion of the
entire outer ring 2112 and cover 2114 ("inward click") toward the
wall, and therefore a very large percentage of the visible portion
of the intelligent thermostat 2100 moves toward the wall during an
inward click. This can be contrasted with the intelligent
thermostat 1900 of FIG. 19, supra, for which an inward pressing of
the outer ring 1906 and/or the glass covering over display monitor
1902 and sensor ring 1904 causes an inward motion of the outer ring
1906 and glass covering toward the stationary circular main body
1908 to reduce the thickness of the peripheral gap 1910, and
therefore a relatively modest percentage of the visible portion of
the intelligent thermostat 1900 moves toward the wall during an
inward click. Behind the Fresnel lens is a passive infrared sensor
2150 for detecting occupancy, i.e., for detecting the presence of a
person who is generally in the same room or space as the
intelligent thermostat 2100. As shown in FIGS. 21A-21D, the front
edge of rotating ring 2112, front face 2114 and Fresnel lens 2110
are shaped such that they together form an integrated convexly
rounded front face that has a common outward arc or spherical shape
gently arcing outward.
[0110] Although being formed from a single lens-like piece of
material such as polycarbonate, the cover 2114 has two different
regions or portions including an outer portion 2114o that is
painted or smoked and a central portion 2114i that is visibly clear
so as to facilitate viewing of an electronic display 2116 disposed
thereunderneath. According to one embodiment in which the diameter
of the intelligent thermostat 2100 is about 80 mm, the diameter of
the electronic display 2116 is about 45 mm. A second
downwardly-tilted PIR sensor 2152 is provided to detect an
approaching user, i.e., a person who is close enough that they may
be intending to physically interact with the intelligent thermostat
2100 and/or may be intending to read the electronic display 2116.
The proximity sensor 2152 can be used to detect proximity in the
range of about one meter so that the intelligent thermostat 2100
can initiate "waking up" when the user is approaching the
thermostat. Such use of proximity sensing is useful for enhancing
the user experience by being "ready" for interaction or viewing as
soon as, or very soon after, the user is ready to interact with or
view the thermostat. Further, the wake-on-proximity functionality
also allows for energy savings within the thermostat by allowing
the electronic display 2116 and certain of its internal components
to "sleep" when no user interaction or viewing is taking place or
about to take place. Intelligent thermostat 2100 further comprises
a rechargeable battery (not shown in FIGS. 21A-21D) and associated
charging circuitry that uses power from the backplate 2142 that is
obtained either via a common wire, if available, or from power
harvesting (sometimes referred to as power stealing, power sharing,
or parasitic powering) from one or more HVAC system call relay
wire(s) if a common wire is not available, as described in U.S.
Ser. No. 13/467,025 (Ref. No. NES0177-US), supra.
[0111] The intelligent thermostat 2100 further includes a
processing system (not shown in FIGS. 21A-21D), a display driver
(not shown in FIGS. 21A-21D), and a wireless communication system
(not shown in FIGS. 21A-21D). The processing system is configured
and programmed to carry out the governance of the operation of
intelligent thermostat 2100 including various user interface
features. The processing system is further programmed and
configured to carry out other operations as described further
hereinabove, hereinbelow and/or in other ones of the commonly
assigned incorporated applications, such as, but not limited to:
automated self-programming of an HVAC schedule based on user
temperature setting inputs and other factors; automated and
optimally-timed determination, based on historical occupancy
statistics, that the house is likely unoccupied and corresponding
automated temperature setback to reduce energy use during those
times; automated prediction of an estimated time to reach a target
temperature and corresponding displayed advisory on the user
interface; automated data logging and uploading of data logs to a
central cloud-based server; automated and optimized control of
particular HVAC equipment, such as heat pumps, based on current and
historical operating statistics and current and forecasted outside
weather; automated gradual schedule migration (with informed user
consent) to lower-energy settings over selected periods of time
such that users are less likely to feel uncomfortable at the
lower-energy temperature setpoints; automated generation and
continuous updating of a home thermal model and HVAC
characteristics for improved HVAC control; automated and optimized
cooperation (with informed user consent) with utility
demand-response event notifications by computation and continual
updating of optimal temperature setpoint trajectories to maximize
selected metrics of energy time-shifting while also minimizing
selected metrics of user discomfort; and a variety of other useful
and energy-saving, comfort-producing functionalities. In
furtherance of these objectives, the wireless communications system
is used to communicate with devices such as home wireless routers,
other thermostats or HVAC system components, or other smart-home
devices, with such communications being, for example, peer-to-peer
communications, communications through one or more servers located
on a private network, and/or communications through a cloud-based
service.
[0112] FIGS. 22A-22B illustrate exploded front and rear perspective
views, respectively, of the intelligent thermostat 2100 with
respect to its two main components, which are the head unit 2140
and the backplate 2142. For ease of installation, configuration
and/or upgrading, especially by a non-expert installer such as a
user, as well as for providing other features and advantages, the
intelligent thermostat 2100 comprises a two-part mechanical
assembly including a head unit 2140 and a backplate 2142. The
backplate 2142 is configured and dimensioned to be affixed to a
wall or other supporting surface, and comprises wiring terminals
for coupling to HVAC wires that lead to the HVAC system being
controlled. The head unit 2140 is removably mountable to the
backplate 2142. Different portions of the functionality of the
intelligent thermostat 2100 are judiciously apportioned between the
head unit 2140 and the backplate 2142 such that a variety of
different goals, objectives, and functionalities are achieved by
virtue of their combination.
[0113] FIGS. 23A-23B illustrate exploded front and rear perspective
views, respectively, of the head unit 2140 with respect to its
primary components. Head unit 2140 includes a back cover 2336, a
bottom frame 2334, a battery assembly 2332, the outer ring 2112
(which is manipulated for ring rotations), a head unit frontal
assembly 2330, the front lens 2114, and the Fresnel lens 2110. Head
unit frontal assembly 2330 includes a top frame 2352 that contains
an LCD module 2362, and further comprises a head unit printed
circuit board 2354 that contains a substantial portion of the head
unit electronic components including processing, memory, wireless
communication, powering and battery charging, and other electronic
components. Electrical components on the head unit frontal assembly
2330 can connect to electrical components on the backplate 2142 by
virtue of ribbon cables and/or other plug type electrical
connectors on back cover 2336. According to some embodiments,
circuitry and components are mounted on both sides of PCB 2354,
while in other embodiments the majority of the circuitry and
components are contained on the forward-facing side (i.e., facing
outward from the wall) of the head unit PCB 2354. An RF shielding
can 2356 (visible in FIG. 23B) surrounds most or all of the
circuitry and components of the head unit PCB 2354 and serves to
shield the circuitry and components from electromagnetic
interference. The portion of RF shielding 2356 that is visible in
FIG. 23B shields the underside of the electrical components on the
forward-facing side of the PCB 2354. The RF shielding 2356 is also
present over the forward-facing side of the PCB 2354 (not visible
in FIG. 23B) such that those components are fully surrounded by RF
shielding.
[0114] Battery assembly 2332 includes a rechargeable Lithium-Ion
battery 2322, which for one preferred embodiment has a nominal
voltage of 3.7 volts and a nominal capacity of 560 mAh. To extend
battery life, however, the battery 2322 is normally not charged
beyond 450 mAh by the thermostat battery charging circuitry.
Moreover, although the battery 2322 is rated to be capable of being
charged to 4.2 volts, the thermostat battery charging circuitry
normally does not charge it beyond 3.95 volts. Battery assembly
2332 also includes connecting wires 2366, and a battery mounting
film 2364 that is attached to battery 2322 using a strong adhesive
and to the rear shielding can 2356 of head unit PCB 2354 using a
relatively weaker adhesive. By using a weaker adhesive to mount the
film 2364 of battery assembly 2332 to shielding can 2356 of the PCB
2354, subsequent replacement of battery assembly 2332 (including
battery 2322) is facilitated. According to some embodiments, the
battery assembly 2332 is user-replaceable.
[0115] FIGS. 24A-24B illustrate additional exploded front and rear
perspective views, respectively, of the head unit 2140, with
further illustration of enumerated components of the head unit
frontal assembly 2330. Head unit frontal assembly 2330 comprises
the head unit top frame 2352, the head unit PCB 2354, and the LCD
module 2161. As illustrated, the optical finger navigation module
2161 (see FIG. 21A, supra) is implemented as a daughter board that
connects to the head unit PCB 2354 and is positioned thereon so
that it can sense rotation of the outer ring 2112. The OFN module
2161 is directed radially outwardly (that is, perpendicular to the
z-axis and away from the center of the thermostat). The OFN module
2161 uses methods analogous to the operation of optical computer
mice to sense the movement of a textured surface on an inner face
of the outer ring 2112. Notably, the OFN module 2161 is one of the
very few sensors that are controlled by a relatively
power-intensive head unit microprocessor rather than a relatively
low-power backplate microprocessor, which are discussed further
with respect to FIGS. 26-27, infra. Among other functions, the
relatively low-power backplate microprocessor is used for polling
and controlling sensors for temperature, humidity, infrared
proximity, ambient light level detection, and inward-click user
inputs so that the relatively high-powered head unit microprocessor
can be in a low-power sleep state for most of the time. Notably,
control of the OFN module 2161 by the head unit microprocessor can
be achieved without confounding this power conservation strategy,
because the head unit processor will already be awake (by virtue of
detection by the PIR motion sensors 2150/2152) by the time the user
manually turns the rotatable ring 2112. Advantageously, very fast
response to the user's turning of the rotatable ring 2112 can be
provided by the head unit microprocessor.
[0116] Also visible in FIGS. 24A-24B is Fresnel lens 2110 that
operates in conjunction with the two PIR motion sensors 2150 and
2152 (see FIG. 21A, supra) that are mounted on a PIR mini-board
2460, which in turn attaches to the back side (i.e., the
wall-facing side) of PCB 2354. Openings at corresponding locations
of the top frame 2352 and PCB 2354 allow infrared radiation that
has passed through Fresnel lens 2110 to impinge upon the PIR motion
sensors 2150 and 2152. Two or more temperature sensors are also
located in the head unit 2140 and cooperate to acquire reliable and
accurate room temperature data. One of the temperature sensors is
located on the daughter board of the OFN module 2161 and the other
is mounted on the head unit PCB 2354.
[0117] FIGS. 25A-25B illustrate exploded front and rear perspective
views, respectively, of the backplate unit 2142. Backplate unit
2142 comprises a backplate rear plate 2582, a backplate circuit
board 2580, and a backplate cover 2570. Visible in FIG. 25A are
HVAC wire connectors 2584 that include integrated mechanical wire
insertion sensing circuitry, and relatively large capacitors 2586
that are used by part of the power stealing circuitry that is
mounted on the backplate circuit board 2580. According to some
embodiments, backplate circuit board 2580 includes a
microcontroller, electronic circuitry that achieves HVAC actuation
functionality (i.e., connecting and disconnecting different pairs
of HVAC wire terminals according to the required HVAC function),
and various functional support and power electronics. For some
embodiments, the HVAC switching and powering functionalities can be
similar to that described in U.S. Ser. No. 13/467,025 (Ref. No.
NES0177-US), supra. For some embodiments, the backplate circuit
board 2580 further contains a temperature/humidity sensor
integrated together in a common IC chip. Wire connectors 2584 are
provided to allow for connection to HVAC system wires, which pass
though the large central circular opening 2590 when the backplate
unit 2142 is mounted to the wall. Also visible in FIGS. 25A-25B are
two mounting holes 2592 and 2594 for use in fixing the backplate to
the wall. The laterally extending mounting holes 2592 and 2594,
together with an integrated bubble level 2572 and bubble level
holder 2574 facilitate leveling during installation without
requiring an external leveling gauge, thereby further enhancing the
ease of a non-expert installation of the intelligent thermostat
2100. FIG. 26 illustrates a front aerial view of the head unit PCB
2354. The head unit circuit board 2354 comprises a head unit
microprocessor 2602, such as a Texas Instruments AM3703 chip, and
an associated oscillator 2603, along with DDR SDRAM memory 2604
(which may be, for example, 64 MB or greater in size), and mass
NAND flash storage 2606 (which may be, for example, 256 MB or
greater in size). A Wi-Fi module 2608, such as a Murata Wireless
Solutions LBWA19XSLZ module, which is based on the Texas
Instruments WL1270 chipset supporting the 802.11 b/g/n WLAN
standard, is provided in a separate compartment of RF shielding
2356 for Wi-Fi capability. Associated with Wi-Fi module 2608 is
supporting circuitry (not shown) such as a 26 MHz crystal
oscillator (not shown). Head unit PCB further comprises an IEEE
802.15.4-compatible wireless communication module 2610, such as an
Ember EM357 chip available from Silicon Laboratories, Inc., also in
a separately shielded RF compartment. Using a protocol that is
compatible with IEEE 802.15.4, which is a standard that specifies
the physical and media access control layers for relatively
low-rate wireless personal area networks, the intelligent
thermostat 2100 may be provided with an ability to communicate
directly with different smart-home sensors for achieving any of a
variety of useful environmental control and monitoring objectives.
Associated with the IEEE 802.15.4 module 2610 is supporting
circuitry such a 24 MHz crystal oscillator (not shown) and a front
end chip 2612, such as a SKY65384 Front-End Module available from
Skyworks Solutions, Inc., that comprises a transmit amplifier, a
low-noise amplifier for receive, and a transmit/receive switch.
Preferably, both the Wi-Fi module 2608 and IEEE 802.15.4 module
2610 are dynamically programmable, with programs for each being
stored in the mass NAND flash storage 2606 and loaded thereon upon
reboot, which provides an ability for respective--new, different,
or updated programs to be downloaded from a central server, stored
in the mass NAND flash storage 2606, and then loaded into the
respective wireless modules 2608/2610.
[0118] Head unit PCB 2354 further includes the PIR mini-board 2460
that is screw-mounted thereto (screws not shown) below the plane
thereof, the PIR mini-board 2460 for supporting the forward-facing
(occupancy detecting) PIR detector 2150, and the
diagonally-downward-facing (proximity detecting) PIR detector 2152,
such that each of the PIR detectors protrudes through an inwardly
extending opening of the head unit PCB 2354. By way of example and
not by way of limitation, the forward-facing (occupancy detecting)
PIR detector 2150 may be a PYD 1794 pyroelectric detector, and the
diagonally-downward-facing (proximity detecting) PIR detector 2152
can be a PYD 5731 pyroelectric detector, both available from
Excelitas Technologies Corp.
[0119] Head unit PCB 2354 further includes a physical/electrical
connector 2622 that mounts thereto inside of an inwardly facing
recess along a perimeter thereof. Mateably attached to the
physical/electrical connector 2622 is a daughterboard 2624 that,
when so attached, rises perpendicular to the head unit PCB 2354
(i.e., in the z-direction in FIG. 26). Mounted on an outwardly
facing surface of the daughterboard 2624 is the optical finger
navigation (OFN) module 2161 (see FIG. 21, supra) for tracking
rotation of the outer ring 2112. By way of example, the OFN module
2161 can be an ADBS-A350 OFN Sensor available from Avago
Technologies. Mounted on an inwardly facing surface of the
daughterboard 2624, and rising substantially above the plane of the
head unit PCB 2354, is a first temperature sensor 2630. Mounted on
the surface of the head unit PCB 2354 is a second temperature
sensor 2632, along with a nearby ambient light sensor (ALS) module
2620. The temperature sensors 2630 and 2632 can each be, for
example, a TMP112 Digital Temperature Sensor available from Texas
Instruments.
[0120] Head unit PCB further includes, inside the RF shielding
2356, battery charging circuitry 2634, preferably including an
LTC4085-3 chip available from Linear Technologies Corporation, or
other devices or circuitry that provides equivalent functionality
and advantages. The LTC4085-3 is a USB power manager and
Li-Ion/Polymer battery charger chip originally developed for
portable battery-powered applications. Head unit PCB 2354 further
includes main power management circuitry 2636 including DC/DC
step-down conversion and voltage regulation circuitry. Head unit
PCB 2354 further includes piezoelectric driving circuitry 2636 for
actuating a piezoelectric buzzer that is mounted on a bottom
surface of a top lid of the RF shielding 2356, for providing
optional audible sounds such as a "tick" sound responsive to user
ring rotations. Head unit PCB 2354 further includes display
backlight voltage generation circuitry 2638.
[0121] Slotted openings 2751 and 2753 are provided in the head unit
PCB 2354 to facilitate mechanical assembly including providing
space for ribbon cables, such as a ribbon cable that runs from the
back side of head unit PCB 2354 to the LCD module 2362 (see FIG.
23A, supra). Two RF antennae (not shown) are provided in the head
unit PCB 2354, the antennae running alongside the respective
slotted openings 2751 and 2753.
[0122] FIG. 27 illustrates an aerial view of the backplate circuit
board 2580. The backplate circuit board 2580 comprises a backplate
processor/microcontroller 2702 that is responsible for generally
orchestrating the operation of the backplate 2142, including
activating the switching circuits coupled to the HVAC wires, and
that is furthermore is responsible for controlling and polling most
of the environmental sensors of the thermostat 2100. The backplate
processor/microcontroller 2702 may comprise, for example, an ST
Micro STM32L151VB microcontroller unit that may include, for
example, an ultra-low-power ARM 32-bit Cortex-M3 CPU, 128 Kbytes of
flash memory, 16 kB of RAM, and various other components such as
direct memory access controllers, analog to digital converters,
input/output ports, communication interfaces, and so forth for
achieving the described functionalities.
[0123] To maximize the amount of time for which the relatively
high-powered head unit processor 2602 of FIG. 26 supra can remain
in a low-power sleep state, it is preferably the relatively
low-powered backplate processor/microcontroller 2702 that controls
and polls not only the sensors contained in the backplate 2142, but
that also controls and polls most of the sensors contained in the
head unit 2140. For one embodiment, in addition to controlling and
polling a temperature/humidity sensor chip 2706 contained on the
backplate circuit board 2580, the backplate
processor/microcontroller 2702 furthermore controls and polls every
sensor contained in the head unit 2140 except for the optical
finger navigation (OFN) module 2161. As such, the backplate
processor/microcontroller 2702 controls and polls the following
components of the head unit 2140: the first temperature sensor
2630, the second temperature sensor 2632, the ambient light sensor
(ALS) 2620, the forward-facing (occupancy detecting) PIR detector
2150, and the diagonally-downward-facing (proximity detecting) PIR
detector 2152. Electrical connectivity between the backplate
processor/microcontroller 2702 and the subject head unit sensors is
achieved using a 20-pin connector 2712, along with one or more port
expanders on the head unit circuit board, by which the electrical
connections are established when the head unit 2140 is mated to the
backplate 2142. Backplate circuit board 2580 further comprises a
timing crystal chip 2704 that may provide, for example, a 32.768
kHz signal that may be used by the backplate
processor/microcontroller 2702 for timing reference purposes. The
backplate processor/microcontroller 2702 performs, in addition to
the controlling and polling of the various sensors, sensing for
mechanical wire insertion at installation, alerting the head unit
regarding current vs. setpoint temperature conditions and actuating
the switches accordingly, and other functions such as looking for
appropriate signals on the inserted HVAC wires at installation and
thereafter.
[0124] Backplate circuit board 2580 further comprises the combined
temperature/humidity sensor chip 2706 positioned near a lower
periphery thereof. The temperature/humidity sensor chip 2706 can
comprise, by way of example and not by way of limitation, a
Sensirion SHT21 module. Thermal isolation of the
temperature/humidity sensor chip 2706 from the rest of the
backplate circuit board 2580 is facilitated by virtue of the lower
mounting through-hole 2594 along with two additional through-holes
2708. Further disposed on the backplate circuit board 2580 in an
arcuate arrangement neararound a peripheral portion of thereof are
the HVAC wire connectors 2584 that include integrated mechanical
wire insertion sensing capability, which are further described in
U.S. Ser. No. 13/624,878 (Ref. No. NES0236-US), supra.
[0125] Backplate circuit board 2580 further comprises encapsulated
transformer/diode circuits 2714 that are used for transformer-based
isolation of the control and logic circuitry from the HVAC
connection and switching circuitry, as described in U.S. Ser. No.
13/034,674 (Ref. No. NES0006-US), supra. Disposed on the back side
of the backplate circuit board 2580 (not shown) are field effect
transistor (FET) switches (not shown) that perform the HVAC
switching functionality and that are electrically disposed on the
isolated HVAC connection and switching circuitry side of the
transformer/diode circuits 2714. The FET switches perform, inter
alia, the basic closing and opening between the requisite HVAC
terminals according to the desired HVAC function. As described in
U.S. Ser. No. 13/467,025 (Ref. No. NES0177-US), supra, one or more
of the FET switches (not shown) are also judiciously configured and
controlled to facilitate power stealing during active HVAC cycles
for circumstances in which there is no "C-wire" available to power
the thermostat 2100. The use of FETs in the switching circuitry
allows for active power stealing, i.e., taking power during the
HVAC ON cycle, by briefly diverting power from the HVAC relay
circuit to the reservoir capacitors for a very small interval, such
as 100 micro-seconds. This time is small enough not to trip the
HVAC relay into the OFF state but is sufficient to charge up the
reservoir capacitors. The use of FETs allows for this fast
switching time (100 micro-seconds), which would be difficult to
achieve using relays (which stay on for tens of milliseconds).
Also, such relays would readily degrade with fast switching, and
they would also make audible noise. In contrast, the FETS operate
with essentially no audible noise.
[0126] Backplate circuit board 2580 further comprises various other
DC-power generation circuitry for providing power that is used by
the backplate processor/microcontroller 2702, the environmental
sensors controlled thereby, the various other backplate circuit
board components, and for providing a DC power rail voltage (e.g.,
4.4 VDC) that is input to the battery charging circuitry of the
head unit 2140. Operation of the various DC-power generation
circuitry is described in U.S. Ser. No. 13/034,674 (Ref. No.
NES0006-US), supra, U.S. Ser. No. 13/034,678 (Ref. No. NES0007-US),
supra, and/or U.S. Ser. No. 13/467,025 (Ref. No. NES0177-US),
supra. Such DC-power generation circuitry includes, for example,
bootstrap and primary LDO circuitry 2716, bridge rectifier
circuitry 2720, the relatively large storage capacitors 2586,
soft-start circuitry 2722, and buck converter circuitry 2724
including inductor 2726. Backplate circuit board 2580 further
comprises wire insertion sensing and voltage sensing circuitry
2728, further portions of which are located on the back side of the
backplate circuit board 2580.
[0127] FIG. 28A illustrates a table of example smart-meter data
recorded by a natural gas smart meter. Second column 2802 lists
examples of the type of smart-meter data recorded by a smart meter.
First column 2801 represents smart-meter data elements of a
smart-meter data vector assigned to numerically represent the
smart-meter data in column 2802. Smart-meter data element s.sub.1
represents the total amount of a utility or service consumed over a
number of subintervals of time, and smart-meter data elements
s.sub.2, s.sub.3, and s.sub.4 represent the amount of the utility
or service consumed in just four subintervals of time. For example,
the utility can be natural gas or electricity provided by a utility
company and the intervals s.sub.2, s.sub.3, and s.sub.4 can each
represent the volume of gas or number of watts consumed during a
particular hour of the day. The smart-meter data element S.sub.1
can represent the total volume of gas or total number of watts
consumed in a 24 hour period.
[0128] FIG. 28B illustrates a table of example operational data
collected and generated by an intelligent thermostat. Second column
2804 represents the types of operational data which can be composed
of control data 2805 entered by an intelligent thermostat user, raw
data 2806 measured by the intelligent thermostat sensors, and data
2807 generated by the intelligent thermostat. First column 2803
represents operational data elements of an operational data vector
assigned to numerically represent the operational data in column
2804. For example, operational data element q.sub.1 represents a
thermostat setting for a first interval of time, q.sub.7 represents
a temperature sensed at the beginning of the first interval, and
q.sub.16 represents the total runtime of an HVAC system. Of course
it is understood that more than one operational data element can be
used to represent different raw data, setpoints and other data
generated by the intelligent thermostat.
[0129] Electricity and natural gas pricing usually peaks at certain
predictable times of the day and season. In particular, if
generation is constrained, prices can rise when electricity and gas
obtained from other regions or more costly sources are brought
online. Because smart meters can record interval consumption of a
service, such as natural gas and electricity, smart-meter data and
other operational data can be used to adjust user consumption
habits to be more responsive to market prices. FIGS. 29A-29H
illustrate examples of the kinds of operational data that can be
collected from and intelligent thermostat and used in calculating
virtual smart-meter data and to adjust schedule settings of an
intelligent thermostat.
[0130] FIG. 29A illustrates a plot of user desired temperatures an
intelligent thermostat is directed to achieve according to the
thermostat settings. The specification indicates that the
temperature is desired to be initially low 2902, then rise to a
high temperature 2904, then subsides to an intermediate temperature
2906, then rise to a relatively high temperature 2908, and then
drop to the low temperature 2902. The temperature control
specification can be visually displayed to the user on the
intelligent thermostat interface or on an organization or vender
website.
[0131] FIG. 29B shows a control schedule corresponding to the
control specification illustrated in FIG. 29A. The control schedule
includes a temperature increase 2910, a temperature decrease 2912,
a temperature increase 2914, and temperature decrease 2916 that
correspond to the user input thermostat settings. The directional
arrows correspond to setpoints, or indications of desired
temperature changes, at particular points in time as set by the
user.
[0132] FIG. 26C illustrates a plot of control output from the
intelligent thermostat to the HVAC system that might result from
the intelligent thermostat attempting to maintain the desired
control specification illustrated in FIG. 26A. Vertical axis 2918
represents the magnitude of a control signal. Each bar represents
the magnitude of an output control signal over time. The control
output may be a current or voltage signal sent from the intelligent
thermostat to a heating unit, with a high-voltage signal indicating
that the heating unit should be currently operating and a low- or
no voltage output indicating that the heating system should not be
operating. The width of each bar represents the amount of time the
heating unit is operating, cross-hatched bars represent the heating
unit operated to increase the temperatures according to the control
schedule, and hash-marked bars represent the heating unit operated
to maintain a desired temperature setting. The times the heating
unit is operated to increase the temperature is denoted by t.sub.h1
and t.sub.h2, the times the heating unit is operated to maintain a
desired temperature is denoted by t.sub.m, and the time between
operating the heating unit to maintain a desired temperature is
denoted by t.sub.d. The intelligent thermostat can calculate and
record the average time to increase the temperature, calculate and
record the average time to maintain a desired temperature, and
calculate and record the time between operating the heating unit to
maintain a desired temperature.
[0133] FIG. 29D illustrates a plot of activity sensed in the home
by the intelligent thermostat motion sensors. Shaded regions 2920
and 2922 represent the level of activity detected by the thermostat
motion sensors in a 24 hour period. In this example, between 1 am
and 5 am no activity is detected in the home, which suggests that
the occupants are sleeping. Between 5 am and 8 am the occupants are
awake and engaged in morning activities, such as preparing to leave
for work or school. Between 8 am and 2 pm no activity is detected
in the home, which suggests that the occupants are away. However,
shaded region 1922 indicates that activity begins at 2 pm and
gradually increases and reduces to no activity at 9 pm.
[0134] FIG. 29E illustrates a plot of virtual smart-meter data that
represents an estimation of the natural gas consumed per hour
during a 24 hour period generated as described above. Spikes
2924-2930 correspond to the time intervals 2931-2936 in FIG. 29C,
respectively, when the heating unit is operated to maintain
temperature according to the control schedule in FIG. 29A. Spikes
2938 and 2940 correspond to time intervals 2942 and 2944 in FIG.
29C, respectively, when the heating unit is operated to increase
the temperature of the home. The remaining elevated portions
represent other levels for natural gas consumption, such as to
operate a gas stove or heat water stored in a hot water heater.
[0135] FIG. 29F illustrates a plot of estimated cost associated
with the estimated natural gas consumption during the 24 hour
period illustrated in FIG. 29E. The cost estimates can be based on
the prices the utility company assigns to low, medium, and high
periods of natural gas consumed during different the time intervals
identified by labeled directional arrows below the time axis. The
shaded regions represent costs that correspond to the natural gas
usage plotted in FIG. 29E. Dark shaded regions 2946 and 2948
represent the cost of consuming natural over the highest price time
intervals. Medium shaded regions 2950-2953 represent the cost of
consuming natural gas over the medium price time intervals. Lightly
shaded regions 2955 and 2956 represent the cost of consuming
natural gas over the lowest price time intervals.
[0136] The data represented in FIGS. 28E and 29F can be presented
to the intelligent-thermostat user via the intelligent thermostat
interface or user interface provided by the intelligent thermostat
manufacturer or vendor. The times to maintain and increase
temperature settings represented in FIG. 29C, the activity levels
represents in FIG. 29D, natural gas levels represented in FIG. 29E,
and the costs represented in FIG. 29F represent just a portion of
the data collected and generated by the intelligent thermostat to
be used in generating virtual smart-meter data and in generating
intelligent thermostat commands and information that can be sent to
the intelligent thermostat to adjust thermostat settings.
[0137] FIG. 29G illustrates a plot of an adjusted control
specification based on the habits of occupants and a decrease in
outside temperatures. For example, occupants may be present before
the setpoint temperature increases as determined by motion sensors
or the outside temperature so that the heating unit can be started
later in order to account for shorter heating time needed to bring
the indoor temperature to a desired high temperature setting. Solid
curve 2658 represents an updated control specification with shorter
periods for the higher temperature settings, and dotted lines 2660
represent the previous control specification illustrated in FIG.
26A.
[0138] FIG. 29H illustrates a plot of an updated control schedule
that corresponds to the updated control schedule illustrated in
FIG. 26A. The solid directional arrows correspond to updated
setpoints, or indications of temperature changes, at particular
points in time as set by the remote system to better reflect the
actual habits of occupants, changing weather conditions, or
increase the energy efficiency of the HVAC system. Dashed
directional arrows represent setpoints that correspond to previous
setpoints illustrated in FIG. 26B.
[0139] Although the current disclosure has been described in terms
of particular examples, it is not intended that the current
disclosure be limited to these examples. Modifications within the
spirit of the disclosure will be apparent to those skilled in the
art. For example, the computational methods can be distributed
between the intelligent controller and the remote system. In
particular, in FIG. 18A, the step 1812 "generate virtual
smart-meter data" can executed in the intelligent controller
instead of the remote system. Because the remote system has access
to the database of operational and smart-meter data for a large
number of intelligent controllers and associated smart meters, the
constants of Equations (1)-(16) or the weight from the feed-forward
neural network can be computed by the remote system and transmitted
to the intelligent controller that, in turn, computes the virtual
smart-meter data. The method logic may include logic-circuit
implementations, firmware, and computer-instruction-based routine
and program implementations, all of which may vary depending on the
selected values of a wide variety of different implementation and
design parameters, including programming language, modular
organization, hardware platform, data structures, control
structures, and many other such design and implementation
parameters.
[0140] It is appreciated that the previous description of the
disclosed examples is provided to enable any person skilled in the
art to make or use the present disclosure. Various modifications to
these examples will be readily apparent to those skilled in the
art, and the generic principles defined herein may be applied to
other examples without departing from the spirit or scope of the
disclosure. Thus, the present disclosure is not intended to be
limited to the examples shown herein but is to be accorded the
widest scope consistent with the principles and novel features
disclosed herein.
* * * * *